Signal interpolation and signal decimation are forms of signal processing which require substantial resource in terms of computer resources and/or circuitry. Signal interpolation and decimation involve discrete samples of an analog signal generally taken at a specific frequency. Signal interpolation produces synthesized discrete signals of the analog signal at a greater frequency. For example, discrete samples of an analog audio signal taken at a frequency of 20 MHz can be interpolated by a 1-to-2 signal interpolator to produce synthesized discrete samples of the analog audio signal at 40 MHz. Signal decimation produces synthesized discrete signals of the analog signal at a lesser frequency.
Signal interpolators and decimators are used to process discrete samples of various types of analog signals to produce discrete samples of the analog signal at various frequencies. For example, signal interpolators and decimators can process discrete samples of an analog seismic signal recorded using a vibration source and one or more geophones in a conventional manner. Alternatively, signal interpolators and decimators can process discrete samples of an analog light signal, i.e., pixels of a color graphical image, recorded by a video camera. Furthermore, signal interpolators and decimators can process discrete samples of an analog audio recording. The discrete samples are typically taken from a source analog signal using an analog-to-digital converter, which converts a particular value of the analog signal at a particular time to a digital number which can be stored in the memory of a computer.
Signal interpolation and decimation typically requires significant amounts of processing resources due in part to the complexity of filters used in interpolation and decimation and due in part to the substantial number of discrete samples processed. A filter is generally a number of weights which are applied to each of a number of discrete samples. The weights are generally referred to as a filter since the weights are applied to various collections of discrete samples. For example, a filter which has twenty-four weights is generally applied, first, to the first through twenty-fourth discrete samples; second, to the second through twenty-fifth discrete samples; third, to the third through twenty-sixth samples; and so on. In the case of a signal interpolator which has twenty-four weights, twenty-four multiplication operations and twenty-three addition operations are required to produce a synthesized discrete sample. Similarly, in the case of a signal decimator which has twenty-four weights, twenty-four multiplication operations and twenty-three addition operations are required to process an original discrete sample of the analog signal.
Signals which are interpolated or decimated typically include substantial numbers of discrete samples. For example, seismic data can involve scores of lines of seismic data, each line representing a path along the surface of the earth and each line including a thousand seismic traces or more. A seismic trace can in turn include thousands of discrete samples of an analog seismic signal measured at a specific point on the surface of the earth. Therefore, a seismic signal can easily include scores of millions of discrete samples. In another example, it is common today for graphical images to have one-thousand or more columns and one-thousand or more rows of picture elements, i.e., pixels, each of which is a discrete sample of a video signal. Such a video signal can be recorded using a video camera, an optical scanner, or can be generated by a computer to represent physical objects defined in part through physical manipulation of computer input devices by a user. Thus, it is common for a graphical image to include a million or more discrete samples of a video image. In addition, motion video signals can include thousands of frames, each of which can include a million or more discrete samples. Accordingly, interpolation or decimation of such seismic or video signals involves processing of millions of signals. Efficiency in a signal interpolator or decimator is therefore highly desirable to reduce the amount of time and resources required to process such signals.
In addition, it is frequently desirable to interpolate or decimate signals very rapidly. For example, a compact disc player typically reads discrete samples of an analog audio signal at a rate of more than 40 million discrete samples per second. To enhance the sound quality of an analog audio signal reproduced from the discrete samples, additional discrete samples are interpolated from the discrete samples retrieved from the compact disc. Such an interpolator must generally process the discrete samples at a rate which is at least the rate at which the discrete samples are retrieved, i.e., at least 40 million discrete samples per second.
Because of the significant processing resources required for such signal interpolation and decimation, a need persists in the industry for ever increasing efficiency in signal interpolators and decimators.